Causal AI Market

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Causal AI Market by Offering (Platforms (Deployment (Cloud and On-premises)) and Services), Vertical (Healthcare & Life Sciences, BFSI, Retail & eCommerce, Transportation & Logistics, Manufacturing), and Region - Global Forecast to 2030

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Report Code: TC 8644 May, 2023, by marketsandmarkets.com

The global market for Causal AI Market is projected to grow from USD 8,010 thousand in 2023 to USD 119,500 thousand by 2030, at a CAGR of 47.1% during the forecast period. Causal AI is driven by the need to understand and reason about cause-and-effect relationships. It allows the progress of Artificial Intelligence from simply recognizing patterns to finding the underlying reasons for those very patterns. It enables counterfactual reasoning, which is a very fundamental element in some areas like exploring alternative scenarios, particularly important in such areas as health and credit risk assessment.

Causal AI Market

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Causal AI Market Opportunities

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Market Dynamics

Driver: The Importance of Causal Inference Models in Various Fields

Causal inference models are better suited for applications where accurate predictions are crucial. They are increasingly being adopted in the healthcare industry for diagnosis, treatment planning, and drug development due to their ability to identify causal relationships between medical conditions and treatments. The finance industry is also driving the growth of the causal AI market, with causal inference models being used for credit risk assessment, fraud detection, and portfolio optimization. Causal inference models provide a more transparent and interpretable approach to predictions, making them suitable for applications where explanations are necessary. This is important for industries such as healthcare and finance, where the ability to explain predictions is critical. In healthcare, causal inference models can identify causal relationships between medical conditions and treatments, leading to more accurate diagnosis, treatment planning, and drug development. In finance, causal inference models are used for credit risk assessment, fraud detection, and portfolio optimization. The ability of causal inference models to identify causal relationships and provide accurate and interpretable predictions is making them increasingly essential for businesses looking to make data-driven decisions.

Restraint: Acquiring and preparing high-quality data

Causal AI models need a huge amount of high-quality data to be trained on, which is impractical to acquire in several domains. In some cases, the data may not exist or are hard to get, while in other cases, the data could be incomplete, noisy, or biased, which leads to inaccurate or unreliable models. Besides the restraint of acquiring high-quality data, there are also challenges associated with the preparation of data for use in causal AI and causal ML models. Causal AI models often require the data to be structured in a very specific way so that the relationships demonstrate a clear cause-effect relationship between the different variables. This requires immense effort and expertise, especially in domains that are complex, with many interacting factors and variables. Researchers and practitioners will especially need to study ways in which data can be acquired and prepared in such a manner that could enable the development of high-quality data to empower Causal Models.

Opportunity: Refined causal inference techniques propel AI advancements

Causal AI offers a significant opportunity to build better AI technologies and algorithms through new approaches to causal inference. This pushes the boundary of artificial intelligence toward higher dimensions, thereby allowing for the discovery of more complex models beyond pattern recognition to understand the real cause of any observed phenomenon. Integrated with techniques for causal inference, an AI system would gain deeper insight and higher accuracy of predictions, thus inspiring innovation across a variety of applications. The more casual AI techniques will get refined, the more they will contribute towards enhancement in capabilities of AI systems for further intelligent and adaptive systems with capability to solve complicated problems with a lot of more precision and effectiveness. It is this transformative potential that places causal AI at the very frontier of next-generation AI technologies, promising significant value in how we understand and apply artificial intelligence.

Challenge: Causal Inference from Complex Data Sets

One of the major challenges of Causal AI is establishing causality from big data sets. While the datasets are larger and more complex, they can hardly establish any kind of causal relationship. It is also true that traditional statistical models lack efficiency in dealing with such complications; therefore, more advanced methods and tools must be designed and developed. Also, these casual relationships might not be immediately apparent and may require extensive analysis to uncover. This challenge is further fueled by noise, incomplete data, and interdependencies between variables. Real data also often arise from dynamic and evolving contexts, thus facing additional challenges to keeping the accuracy of the causal inferences over time. This level of complexity remains one of the big challenges to causal AI, which has to move toward the accuracy of inference across industries and size, in addition to the complexity of large, multifaceted data sets.

Causal AI Market Ecosystem

Top Companies in Causal AI Market

By deployment, cloud to account for the largest market size during the forecast period

The cloud-based deployment model allows organizations flexibility in accessing the most powerful tools of causal inference with scalability and affordability. Real-time data processing integrated with other advanced technologies, such as machine learning and big data analytics, adds more popularity to the cloud for applications based on causal AI. Organizations working with Causal AI go all out in adopting the cloud-based solution for seamless access, collaboration, and scalability. This trend is highly visible in verticals such as healthcare, finance, and retail, where the ability to scale faster in deploying AI-driven insights becomes critical to competitiveness. Therefore, the cloud segment will dominate the market for casual AI due to the robust infrastructure it provides for further growth and innovation in AI technologies.

By offering, platform segment to account for the largest market size during the forecast period

The intensive adoption of causal AI platforms is due to the high demand for end-to-end solutions to build, implement, and manage applications based on causal AI technologies. These platforms ensure that all the services that are required to carry out causal analysis, such as data pre-processing, model building, visualization, and assistance in decision-making, are all provided from one location. The platform interface abstracts away the difficult task of dealing with numerous tools and technologies required for developing ML-based models. Platform solutions enable companies to extract maximum value from their available data by leveraging complex algorithms provided by vendors or other users on their proprietary or open-source platforms. Moreover, industries including healthcare, finance, and manufacturing require accurate identification of cause-and-effect relationships for efficient decision-making and operations.

North America to account for the largest market size during the forecast period

North America is one of the most important regions in the development and enhancement of Causal AI. Causal AI is gaining momentum since more businesses and organizations have a dire need for higher levels of analytics to gain deep insights and make better decisions. The region is home to leading AI technology companies, research institutions, and innovative start-ups that drive new developments and adoptions of causal AI. The governments of the US and Canada have already started development and deployment-related initiatives to do with AI. The US has developed the National Institute of Standards and Technology for developing standards and guidelines related to the use of AI in various industries, healthcare, and finance.

Causal AI Market Size, and Share

Key Market Players

The Causal AI vendors have implemented various types of organic and inorganic growth strategies, such as new product launches, product upgrades, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global market for Causal AI are IBM (US), CausaLens (England), Microsoft (US), Causaly (England), Google (US), Geminos (US), AWS (US), Aitia (US), INCRMNTAL (Israel), Logility (US), Cognino.ai. (England), H2O.ai (US), DataRobot (US), Cognizant (US), Scalnyx (France), Causality Link (US), Dynatrace (US), Parabole.ai (US), Causalis.ai (Israel), and Omics Data Automation (US).

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Scope of the Report

Report Metrics	Details
Market size available for years	2020–2030
Base year considered	2022
Forecast period	2023–2030
Forecast units	USD Million
Segments covered	Offering, Vertical, and Region
Geographies covered	North America, Europe and Rest of World
Companies covered	IBM (US), CausaLens (UK), Microsoft (US), Causaly (UK), Google (US), Geminos (US), AWS (US), Aitia (US), Xplain Data (Germany), INCRMNTAL (Israel), Logility (US), Cognino.ai. (UK), H2O.ai (US), DataRobot (US), Cognizant (US), Scalnyx (France), Causality Link (US), Dynatrace (US), Parabole.ai (US) and datma (US).

This research report categorizes the Causal AI market based on Offering, Vertical, and Region.

By Offering:

Platform
- By Deployment:
  - Cloud
  - On-premises
Services
- Consulting Services
- Deployment & Integration
- Training, Support, and Maintenance

By Vertical:

Healthcare & Lifesciences
BFSI
Retail & eCommerce
Tansportation & Logistics
Manufacturing
Other Verticals

By Region:

North America
- US
- Canada
Europe
- UK
- Germany
- France
- Rest of Europe
Rest of World
- Israel
- China
- Japan
- Rest of the RoW

Recent Developments:

In February 2023, Dynatrace introduced new capabilities to Grail that enable boundless exploratory analysis by adding new data types and unlocking support for graph analytics. These capabilities enable Davis, the Dynatrace causal AI engine, to gather even more insights.
In January 2023, CausaLens released a new operating system for decision-making powered by causal AI. The system is designed to support organizations in making more accurate predictions and optimize their business processes.
In December 2022, Microsoft launched a causal AI suite (DoWhy, EconML, Causica, and ShowWhy) for decision-making that enables developers and data scientists to build models that provide causal explanations for their predictions. The suite includes the DoWhy, EconML, and CausalML libraries, and is integrated with Azure Machine Learning and Azure Databricks.
In June 2022, Microsoft announced the collaboration with AWS to develop a new home for DoWhy on GitHub, which will not only increase the library's availability but also give Microsoft an edge over competitors in the causal machine learning space.
In, September 2021, IBM introduced Causal Inference 360 Toolkit. The toolkit offers powerful tools and algorithms to conduct Causal Inference with unique scalability on a raft of applications for businesses and researcher teams seeking to learn such knowledge from the system at play.

Frequently Asked Questions (FAQ):

What is Causal AI?

Causal AI is an advanced form of AI and machine learning (ML) that focuses on understanding and modeling cause-and-effect relationships. While the traditional AI system identifies the patterns and correlation between the data, causal AI aims at discovering the underlying cause that drives those patterns. This allows the AI systems to foresee events and understand the reason behind particular events, which results in much more accurate and meaningful insights.

Which countries are considered in the European region?

The report includes an analysis of the UK, Germany, and France in the European region.

Which are key verticals adopting Causal AI platforms and services?

Key verticals adopting Causal AI platform and services include Healthcare & Lifesciences, BFSI, Retail & eCommerce, Transportation & Logistics, Manufacturing, and others verticals, which comprise IT/ITeS, government and defense, and telecom.

Which are the key drivers supporting the market growth for Causal AI?

The key drivers supporting the market growth for Causal AI include importance of causal inference models in various fields, emergence of Causal AI as a solution to overcome the limitations of current AI and operationalizing AI initiatives.

Who are the key vendors in the market for Causal AI?

The key vendors in the global Causal AI market include IBM (US), CausaLens (England), Microsoft (US), Causaly (England), Google (US), Geminos (US), AWS (US), Aitia (US), INCRMNTAL (Israel), Logility (US), Cognino.ai. (England), H2O.ai (US), DataRobot (US), Cognizant (US), Scalnyx (France), Causality Link (US), Dynatrace (US), Parabole.ai (US), Causalis.ai (Israel), and Omics Data Automation (US). .

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TABLE OF CONTENTS

1 INTRODUCTION (Page No. - 22)

1.1 STUDY OBJECTIVES

1.2 MARKET DEFINITION

1.3 INCLUSIONS AND EXCLUSIONS

1.4 MARKET SCOPE

1.4.1 MARKET SEGMENTATION

1.4.2 REGIONS COVERED

1.4.3 YEARS CONSIDERED

1.5 CURRENCY CONSIDERED

TABLE 1 USD EXCHANGE RATES, 2020–2022

1.6 STAKEHOLDERS

2 RESEARCH METHODOLOGY (Page No. - 26)

2.1 RESEARCH DATA

FIGURE 1 CAUSAL AI MARKET: RESEARCH DESIGN

2.1.1 SECONDARY DATA

2.1.2 PRIMARY DATA

2.1.2.1 Primary interviews

2.1.2.2 Breakup of primary profiles

2.1.2.3 Key industry insights

2.2 DATA TRIANGULATION

2.3 MARKET SIZE ESTIMATION

FIGURE 2 MARKET: TOP-DOWN AND BOTTOM-UP APPROACHES

2.3.1 TOP-DOWN APPROACH

2.3.2 BOTTOM-UP APPROACH

FIGURE 3 APPROACH 1 (SUPPLY SIDE): REVENUE FROM OFFERING OF CAUSAL AI MARKET

FIGURE 4 APPROACH 2—BOTTOM-UP (SUPPLY SIDE): COLLECTIVE REVENUE FROM OFFERING OF CAUSAL AI PLAYERS

FIGURE 5 APPROACH 3—BOTTOM-UP (SUPPLY SIDE): REVENUE AND SUBSEQUENT MARKET ESTIMATION FROM OFFERING OF CAUSAL AI

FIGURE 6 APPROACH 4—BOTTOM-UP (DEMAND SIDE): SHARE OF CAUSAL AI OFFERING THROUGH OVERALL CAUSAL AI SPENDING

2.4 MARKET FORECAST

TABLE 2 FACTOR ANALYSIS

2.5 ASSUMPTIONS

TABLE 3 RESEARCH ASSUMPTIONS

2.6 LIMITATIONS

2.7 IMPLICATION OF RECESSION ON GLOBAL CAUSAL AI MARKET

TABLE 4 IMPACT OF RECESSION ON GLOBAL MARKET

3 EXECUTIVE SUMMARY (Page No. - 39)

TABLE 5 CAUSAL AI MARKET SIZE AND GROWTH RATE, 2020–2022 (USD THOUSAND, Y-O-Y)

TABLE 6 MARKET SIZE AND GROWTH RATE, 2023–2030 (USD THOUSAND, Y-O-Y)

FIGURE 7 CAUSAL AI PLATFORMS TO ACCOUNT FOR LARGER MARKET THAN SERVICES IN 2023

FIGURE 8 CLOUD DEPLOYMENT TO ACCOUNT FOR LARGER MARKET SHARE IN 2023

FIGURE 9 CONSULTING SERVICES TO ACCOUNT FOR LARGEST MARKET IN 2023

FIGURE 10 HEALTHCARE & LIFESCIENCES VERTICAL TO ACCOUNT FOR LARGEST MARKET IN 2023

FIGURE 11 NORTH AMERICA ESTIMATED TO ACCOUNT FOR LARGEST SHARE IN 2023

4 PREMIUM INSIGHTS (Page No. - 43)

4.1 ATTRACTIVE OPPORTUNITIES IN CAUSAL AI MARKET

FIGURE 12 INCRASING DEMAND FOR DATA-DRIVEN DECISION MAKING TO EXPAND THE MARKET FOR CAUSAL AI PLAFORMS

4.2 MARKET, BY VERTICAL

FIGURE 13 HEALTHCARE & LIFE SCIENCES TO ACCOUNT FOR LARGEST SIZE DURING FORECAST PERIOD

4.3 MARKET, BY REGION

FIGURE 14 NORTH AMERICA TO ACCOUNT FOR LARGEST SHARE BY 2023

4.4 MARKET, BY OFFERING AND KEY VERTICAL

FIGURE 15 PLATFORMS AND HEALTHCARE & LIFE SCIENCES SEGMENTS TO ACCOUNT FOR SIGNIFICANT RESPECTIVE SHARES BY 2030

5 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. - 45)

5.1 INTRODUCTION

5.2 MARKET DYNAMICS

FIGURE 16 CAUSAL AI MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES

5.2.1 DRIVERS

5.2.1.1 Importance of causal inference models in various fields

5.2.1.2 Emergence of causal AI to overcome limitations of current AI

5.2.1.3 Operationalizing AI initiatives

5.2.2 RESTRAINTS

5.2.2.1 Lack of interpretability and explainability

5.2.2.2 Acquiring and preparing high-quality data

5.2.3 OPPORTUNITIES

5.2.3.1 Potential to revolutionize healthcare field

5.2.3.2 Technological advancements

5.2.4 CHALLENGES

5.2.4.1 Causal inference from complex data sets

5.2.4.2 Lack of standardization

5.3 CASE STUDY ANALYSIS

5.3.1 ACCELERATING MODEL VALIDATION WITH CAUSAL AI

5.3.2 UNLOCKING REVENUE GROWTH WITH CAUSAL AI-POWERED PRICING AND PROMOTION OPTIMIZATION

5.3.3 USING CAUSAL AI TO ENHANCE CUSTOMER RETENTION STRATEGIES

5.3.4 REVOLUTIONIZING DATA PROVIDER INDUSTRY WITH CAUSAL AI

5.3.5 USE OF CAUSAL AI FOR CUSTOMER SEGMENTATION

5.3.6 CASE STUDY 6: CAUSAL AI TO IMPROVE MANUFACTURING PROCESSES

5.4 BRIEF HISTORY OF EVOLUTION OF CAUSAL AI

5.5 ECOSYSTEM ANALYSIS

FIGURE 17 ECOSYSTEM ANALYSIS

TABLE 7 PLATFORM PROVIDERS

TABLE 8 LIBRARY PROVIDERS

TABLE 9 AI FRAMEWORK PROVIDERS

TABLE 10 REGULATORY BODIES

5.6 KEY STEPS IN USING CAUSAL AI

5.6.1 DATA COLLECTION & PREPARATION

5.6.2 CAUSAL INFERENCE

5.6.3 ML MODELS

5.6.4 INTERPRETABILITY & EXPLAINABILITY

5.6.5 VALIDATION & TESTING

5.7 CORRELATION-BASED AI VS. CAUSAL AI

TABLE 11 CORRELATION-BASED AI VS CAUSAL AI

5.8 TECHNOLOGY ANALYSIS

5.8.1 RELATED TECHNOLOGIES

5.8.1.1 Supervised learning

5.8.1.2 Unsupervised learning

5.8.1.3 Natural language processing

5.8.1.4 Predictive analytics

5.8.1.5 Deep learning

5.8.1.6 AI governance (ethical, explainable, and responsible AI)

5.8.1.7 Bayesian networks

5.8.2 ALLIED TECHNOLOGIES

5.8.2.1 Cloud computing

5.8.2.2 Robotics

5.8.2.3 Federated learning

5.8.2.4 Digital twin

5.9 BEST PRACTICES IN CAUSAL AI MARKET

5.10 FUTURE DIRECTIONS OF CAUSAL AI LANDSCAPE

TABLE 12 SHORT-TERM ROADMAP, 2023–2025

TABLE 13 MID-TERM ROADMAP, 2026–2028

TABLE 14 LONG-TERM ROADMAP, 2029–2030

5.11 VALUE CHAIN ANALYSIS

FIGURE 18 MARKET: VALUE CHAIN ANALYSIS

5.11.1 DATA COLLECTION & PREPARATION

5.11.2 ALGORITHM DEVELOPMENT

5.11.3 MODEL TRAINING

5.11.4 MODEL TESTING & VALIDATION

5.11.5 DEPLOYMENT & INTEGRATION

5.11.6 MAINTENANCE & SUPPORT

5.12 PRICING MODEL ANALYSIS

TABLE 15 PRICING MODELS

5.13 PATENT ANALYSIS

5.13.1 METHODOLOGY

5.13.2 DOCUMENT TYPE

TABLE 16 PATENTS FILED, 2013–2023

5.13.3 INNOVATION & PATENT APPLICATIONS

FIGURE 19 TOTAL NUMBER OF PATENTS GRANTED, 2013–2023

5.13.3.1 Top Applicants

FIGURE 20 TOP TEN COMPANIES WITH HIGHEST NUMBER OF PATENT APPLICATIONS, 2013–2022

TABLE 17 US: TOP 20 PATENT OWNERS, 2013–2022

TABLE 18 LIST OF PATENTS IN CAUSAL AI MARKET, 2021–2023

5.14 PORTER’S FIVE FORCES ANALYSIS

FIGURE 21 PORTER’S FIVE FORCES ANALYSIS

TABLE 19 PORTER’S FIVE FORCES ANALYSIS

5.14.1 THREAT FROM NEW ENTRANTS

5.14.2 THREAT FROM SUBSTITUTES

5.14.3 BARGAINING POWER OF SUPPLIERS

5.14.4 BARGAINING POWER OF BUYERS

5.14.5 INTENSITY OF COMPETITIVE RIVALRY

5.15 REGULATORY LANDSCAPE

5.15.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 20 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 21 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 22 ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

TABLE 23 ROW: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS

5.15.1.1 North America

5.15.1.1.1 US

5.15.1.1.2 Canada

5.15.1.2 Europe

5.15.1.3 Asia Pacific

5.15.1.3.1 South Korea

5.15.1.3.2 China

5.15.1.3.3 India

5.15.1.4 Middle East & Africa

5.15.1.4.1 UAE

5.15.1.4.2 KSA

5.15.1.4.3 Bahrain

5.15.1.5 Latin America

5.15.1.5.1 Brazil

5.15.1.5.2 Mexico

5.16 KEY STAKEHOLDERS AND BUYING CRITERIA

5.16.1 KEY STAKEHOLDERS IN BUYING PROCESS

FIGURE 22 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS IN TOP THREE VERTICALS

TABLE 24 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS IN TOP THREE VERTICALS

5.16.2 BUYING CRITERIA

FIGURE 23 KEY BUYING CRITERIA IN TOP THREE VERTICALS

TABLE 25 KEY BUYING CRITERIA IN TOP THREE VERTICALS

5.17 DISRUPTIONS IMPACTING BUYERS/CLIENTS IN CAUSAL AI MARKET

FIGURE 24 DISRUPTIONS IMPACTING BUYERS/CLIENTS

5.18 KEY CONFERENCES & EVENTS

TABLE 26 DETAILED LIST OF CONFERENCES & EVENTS, 2023–2024

5.19 BUSINESS MODELS OF CAUSAL AI

5.19.1 POTENTIAL OUTCOME FRAMEWORK

5.19.2 CAUSAL GRAPH MODEL

5.20 APPROACHES TO CAUSAL INFERENCES

5.20.1 CORRELATIONS

5.20.2 CAUSATION

5.20.3 INTERVENTIONS

5.20.4 COUNTERFACTUALS

5.20.5 SYSTEM MODELING

5.21 CAUSAL AI TECHNIQUES & METHODS

5.21.1 MACHINE LEARNING ALGORITHMS

5.21.1.1 Regression-based methods

5.21.1.2 Decision trees and random forests

5.21.1.3 K-nearest neighbor algorithms

5.21.1.4 Other ML algorithms

5.21.2 BAYESIAN NETWORKS

5.21.2.1 Directed acyclic graphs (DAGs)

5.21.2.2 Structural causal models (SCMs)

5.21.2.3 Counterfactual DAGs

5.21.2.4 Other Bayesian networks

5.21.3 STRUCTURAL EQUATION MODELS

5.21.3.1 Path analysis (DAGs)

5.21.3.2 Confirmatory factor analysis (CFA)

5.21.3.3 Partial least squares (PLS)

5.21.3.4 Other structural equation models

5.21.4 COUNTERFACTUAL ANALYSIS

5.21.4.1 Propensity score matching (PSM)

5.21.4.2 Difference-in-Differences (DiD)

5.21.4.3 Instrumental variables (IV)

5.21.4.4 Regression discontinuity design (RDD)

6 CAUSAL AI MARKET, BY OFFERING (Page No. - 87)

6.1 INTRODUCTION

6.1.1 OFFERING: MARKET DRIVERS

FIGURE 25 CAUSAL AI SERVICES MARKET TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD

TABLE 27 MARKET, BY OFFERING, 2020–2022 (USD THOUSAND)

TABLE 28 MARKET, BY OFFERING, 2023–2030 (USD THOUSAND)

6.2 PLATFORMS

6.2.1 DEMAND FOR DATA-DRIVEN DECISION-MAKING AND MORE ACCURATE PREDICTIONS AND INSIGHTS

TABLE 29 PLATFORMS: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 30 PLATFORMS: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

6.2.2 CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT

FIGURE 26 ON-PREMISE PLATFORM DEPLOYMENT TO WITNESS HIGHER CAGR DURING FORECAST PERIOD

TABLE 31 CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2020–2022 (USD THOUSAND)

TABLE 32 CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2023–2030 (USD THOUSAND)

6.2.2.1 On-premises

6.2.2.1.1 Potential for greater customization and integration

TABLE 33 ON-PREMISES: CAUSAL AI PLATFORMS MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 34 ON-PREMISES: CAUSAL AI PLATFORMS MARKET, BY REGION, 2023–2030 (USD THOUSAND)

6.2.2.2 Cloud

6.2.2.2.1 Potential for greater accessibility

TABLE 35 CLOUD: CAUSAL AI PLATFORMS MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 36 CLOUD: CAUSAL AI PLATFORMS MARKET, BY REGION, 2023–2030 (USD THOUSAND)

6.3 SERVICES

6.3.1 VALUABLE RESOURCES AVAILABLE FOR THOSE LACKING INTERNAL PROFICIENCY

FIGURE 27 TRAINING, SUPPORT, AND MAINTENANCE SERVICES TO ACCOUNT FOR LARGEST MARKET DURING FORECAST PERIOD

TABLE 37 CAUSAL AI MARKET, BY SERVICE, 2020–2022 (USD THOUSAND)

TABLE 38 MARKET, BY SERVICE, 2023–2030 (USD THOUSAND)

TABLE 39 SERVICES: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 40 SERVICES: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

6.3.2 CONSULTING SERVICES

6.3.2.1 Expert guidance to make informed decisions and achieve better results

TABLE 41 CONSULTING SERVICES: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 42 CONSULTING SERVICES: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

6.3.3 DEPLOYMENT & INTEGRATION

6.3.3.1 Focus on practical aspects of implementing causal inference

TABLE 43 DEPLOYMENT & INTEGRATION: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 44 DEPLOYMENT & INTEGRATION: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

6.3.4 TRAINING, SUPPORT, AND MAINTENANCE

6.3.4.1 Need for ongoing training and support to ensure optimal model performance and accuracy

TABLE 45 TRAINING, SUPPORT, AND MAINTENANCE: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 46 TRAINING, SUPPORT, AND MAINTENANCE: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

7 CAUSAL AI MARKET, BY VERTICAL (Page No. - 97)

7.1 INTRODUCTION

7.1.1 VERTICAL: MARKET DRIVERS

FIGURE 28 HEALTHCARE & LIFE SCIENCES VERTICAL TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD

TABLE 47 MARKET, BY VERTICAL, 2020–2022 (USD THOUSAND)

TABLE 48 MARKET, BY VERTICAL, 2023–2030 (USD THOUSAND)

7.2 BFSI

7.2.1 HIGHLY COMPETITIVE WITH SEVERAL OPERATIONAL PLAYERS

TABLE 49 BFSI: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 50 BFSI: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

7.2.2 USE CASES: BFSI

7.3 HEALTHCARE & LIFE SCIENCES

7.3.1 INVESTMENT BY STARTUPS IN DEVELOPING BLOOD TESTS FOR EARLY CANCER DETECTION

TABLE 51 HEALTHCARE & LIFE SCIENCES: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 52 HEALTHCARE & LIFE SCIENCES: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

7.3.2 USE CASES: HEALTHCARE & LIFE SCIENCES

7.4 RETAIL & ECOMMERCE

7.4.1 OPTIMIZING PRODUCT INVENTORY FOR RETAILERS AND DISCOVERY FOR CUSTOMERS

TABLE 53 RETAIL & ECOMMERCE: CAUSAL AI MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 54 RETAIL & ECOMMERCE: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

7.4.2 USE CASES: RETAIL & ECOMMERCE

7.5 MANUFACTURING

7.5.1 ANALYZING DATA FROM PRODUCTION PROCESSES TO IDENTIFY DEFECTS AND QUALITY ISSUES IN REAL TIME

TABLE 55 MANUFACTURING: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 56 MANUFACTURING: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

7.5.2 USE CASES: MANUFACTURING

7.6 TRANSPORTATION & LOGISTICS

7.6.1 OPTIMIZING VEHICLE ROUTES, TRACKING SHIPMENTS IN REAL TIME, AND IMPROVING DELIVERY TIMES

TABLE 57 TRANSPORTATION & LOGISTICS: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 58 TRANSPORTATION & LOGISTICS: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

7.6.2 USE CASES: TRANSPORTATION & LOGISTICS

7.7 OTHER VERTICALS

TABLE 59 OTHER VERTICALS: MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 60 OTHER VERTICALS: MARKET, BY REGION, 2023–2030 (USD THOUSAND)

8 CAUSAL AI MARKET, BY REGION (Page No. - 111)

8.1 INTRODUCTION

FIGURE 29 NORTH AMERICA TO BE LARGEST MARKET DURING FORECAST PERIOD

FIGURE 30 JAPAN TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD

TABLE 61 MARKET, BY REGION, 2020–2022 (USD THOUSAND)

TABLE 62 MARKET, BY REGION, 2023–2030 (USD THOUSAND)

8.2 NORTH AMERICA

8.2.1 NORTH AMERICA: MARKET DRIVERS

8.2.2 NORTH AMERICA: IMPACT OF RECESSION

FIGURE 31 NORTH AMERICA: CAUSAL AI MARKET SNAPSHOT

TABLE 63 NORTH AMERICA: MARKET, BY OFFERING, 2020–2022 (USD THOUSAND)

TABLE 64 NORTH AMERICA: MARKET, BY OFFERING, 2023–2030 (USD THOUSAND)

TABLE 65 NORTH AMERICA: CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2020–2022 (USD THOUSAND)

TABLE 66 NORTH AMERICA: CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2023–2030 (USD THOUSAND)

TABLE 67 NORTH AMERICA: MARKET, BY SERVICE, 2020–2022 (USD THOUSAND)

TABLE 68 NORTH AMERICA: MARKET, BY SERVICE, 2023–2030 (USD THOUSAND)

TABLE 69 NORTH AMERICA: MARKET, BY VERTICAL, 2020–2022 (USD THOUSAND)

TABLE 70 NORTH AMERICA: MARKET, BY VERTICAL, 2023–2030 (USD THOUSAND)

TABLE 71 NORTH AMERICA: MARKET, BY COUNTRY, 2020–2022 (USD THOUSAND)

TABLE 72 NORTH AMERICA: MARKET, BY COUNTRY, 2023–2030 (USD THOUSAND)

8.2.3 US

8.2.3.1 Research and investment by leading universities and organizations

8.2.4 CANADA

8.2.4.1 Rise in adoption of machine learning applications in various industries

8.3 EUROPE

8.3.1 EUROPE: MARKET DRIVERS

8.3.2 EUROPE: IMPACT OF RECESSION

TABLE 73 EUROPE: CAUSAL AI MARKET, BY OFFERING, 2020–2022 (USD THOUSAND)

TABLE 74 EUROPE: MARKET, BY OFFERING, 2023–2030 (USD THOUSAND)

TABLE 75 EUROPE: CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2020–2022 (USD THOUSAND)

TABLE 76 EUROPE: CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2023–2030 (USD THOUSAND)

TABLE 77 EUROPE: MARKET, BY SERVICE, 2020–2022 (USD THOUSAND)

TABLE 78 EUROPE: MARKET, BY SERVICE, 2023–2030 (USD THOUSAND)

TABLE 79 EUROPE: MARKET, BY VERTICAL, 2020–2022 (USD THOUSAND)

TABLE 80 EUROPE: MARKET, BY VERTICAL, 2023–2030 (USD THOUSAND)

TABLE 81 EUROPE: MARKET, BY COUNTRY, 2020–2022 (USD THOUSAND)

TABLE 82 EUROPE: MARKET, BY COUNTRY, 2023–2030 (USD THOUSAND)

8.3.3 UK

8.3.3.1 Businesses increasingly seeking to leverage benefits of AI and ML

8.3.4 GERMANY

8.3.4.1 Strong IT infrastructure and robust regulatory framework

8.3.5 FRANCE

8.3.5.1 Thriving startup ecosystem

8.3.6 REST OF EUROPE

8.4 REST OF THE WORLD (ROW)

8.4.1 REST OF THE WORLD: MARKET DRIVERS

8.4.2 ROW: IMPACT OF RECESSION

TABLE 83 ROW: CAUSAL AI MARKET, BY OFFERING, 2020–2022 (USD THOUSAND)

TABLE 84 ROW: MARKET, BY OFFERING, 2023–2030 (USD THOUSAND)

TABLE 85 ROW: CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2020–2022 (USD THOUSAND)

TABLE 86 ROW: CAUSAL AI PLATFORMS MARKET, BY DEPLOYMENT, 2023–2030 (USD THOUSAND)

TABLE 87 ROW: MARKET, BY SERVICE, 2020–2022 (USD THOUSAND)

TABLE 88 ROW: MARKET, BY SERVICE, 2023–2030 (USD THOUSAND)

TABLE 89 ROW: MARKET, BY VERTICAL, 2020–2022 (USD THOUSAND)

TABLE 90 ROW: MARKET, BY VERTICAL, 2023–2030 (USD THOUSAND)

TABLE 91 ROW: MARKET, BY COUNTRY, 2020–2022 (USD THOUSAND)

TABLE 92 ROW: MARKET, BY COUNTRY, 2023–2030 (USD THOUSAND)

8.4.3 ISRAEL

8.4.3.1 Adoption of AI-based solutions in healthcare

8.4.4 CHINA

8.4.4.1 Initiatives such as Next Generation Artificial Intelligence Development Plan

8.4.5 JAPAN

8.4.5.1 Dedicated research initiatives such as Artificial Intelligence Technology Strategy

8.4.6 OTHERS IN ROW

9 COMPETITIVE LANDSCAPE (Page No. - 129)

9.1 OVERVIEW

9.2 KEY PLAYER STRATEGIES

TABLE 93 OVERVIEW OF KEY PRODUCTS LAUNCHED BY PROMINENT PLAYERS IN MARKET

9.3 MARKET SHARE ANALYSIS

FIGURE 32 MARKET SHARE ANALYSIS FOR KEY PLAYER, 2022

TABLE 94 MARKET: INTENSITY OF COMPETITIVE RIVALRY

9.4 COMPANY EVALUATION QUADRANT

9.4.1 STARS

9.4.2 EMERGING LEADERS

9.4.3 PERVASIVE PLAYERS

9.4.4 PARTICIPANTS

FIGURE 33 KEY CAUSAL AI MARKET PLAYERS, COMPANY EVALUATION MATRIX, 2023

9.5 COMPETITIVE BENCHMARKING

TABLE 95 COMPETITIVE BENCHMARKING OF KEY PLAYERS, 2022

TABLE 96 DETAILED LIST OF KEY STARTUPS/SMES

TABLE 97 COMPETITIVE BENCHMARKING OF STARTUPS/SMES

9.6 CAUSAL AI PRODUCT LANDSCAPE

9.6.1 COMPARATIVE ANALYSIS OF CAUSAL AI PRODUCTS

TABLE 98 COMPARATIVE ANALYSIS OF CAUSAL AI PRODUCTS

FIGURE 34 COMPARATIVE ANALYSIS OF CAUSAL AI PRODUCTS

9.6.2 VALUATION AND FINANCIAL METRICS OF KEY CAUSAL AI VENDORS

FIGURE 35 FINANCIAL METRICS OF KEY CAUSAL AI VENDORS

FIGURE 36 YTD PRICE TOTAL RETURN AND STOCK BETA OF KEY CAUSAL AI VENDORS

9.7 COMPETITIVE SCENARIO

9.7.1 PRODUCT LAUNCHES

TABLE 99 PRODUCT LAUNCHES, MAY 2021–FEBRUARY 2023

9.7.2 DEALS

TABLE 100 DEALS, OCTOBER 2020–FEBRUARY 2023

10 COMPANY PROFILES (Page No. - 139)

10.1 INTRODUCTION

(Business overview, Products/Solutions/Services offered, Recent developments & MnM View)*

10.2 KEY PLAYERS

10.2.1 IBM

TABLE 101 IBM: BUSINESS OVERVIEW

FIGURE 37 IBM: COMPANY SNAPSHOT

TABLE 102 IBM: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 103 IBM: PRODUCT LAUNCHES

TABLE 104 IBM: DEALS

10.2.2 MICROSOFT

TABLE 105 MICROSOFT: BUSINESS OVERVIEW

FIGURE 38 MICROSOFT: COMPANY SNAPSHOT

TABLE 106 MICROSOFT: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 107 MICROSOFT: PRODUCT LAUNCHES

TABLE 108 MICROSOFT: DEALS

10.2.3 GOOGLE

TABLE 109 GOOGLE: BUSINESS OVERVIEW

FIGURE 39 GOOGLE: FINANCIAL OVERVIEW

TABLE 110 GOOGLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 111 GOOGLE: PRODUCT LAUNCHES

TABLE 112 GOOGLE: DEALS

10.2.4 AWS

TABLE 113 AWS: BUSINESS OVERVIEW

FIGURE 40 AWS: FINANCIAL OVERVIEW

TABLE 114 AWS: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 115 AWS: PRODUCT LAUNCHES

TABLE 116 AWS: DEALS

10.2.5 DYNATRACE

TABLE 117 DYNATRACE: BUSINESS OVERVIEW

FIGURE 41 DYNATRACE: FINANCIAL OVERVIEW

TABLE 118 DYNATRACE: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 119 DYNATRACE: PRODUCT LAUNCHES

TABLE 120 DYNATRACE: DEALS

10.2.6 H2O.AI

TABLE 121 H2O.AI: BUSINESS OVERVIEW

TABLE 122 H2O.AI: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 123 H2O.AI: PRODUCT LAUNCHES

TABLE 124 H2O.AI: DEALS

10.2.7 DATAROBOT

TABLE 125 DATAROBOT: BUSINESS OVERVIEW

TABLE 126 DATAROBOT: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 127 DATAROBOT: DEALS

10.2.8 CAUSALENS

TABLE 128 CAUSALENS: BUSINESS OVERVIEW

TABLE 129 CAUSALENS: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 130 CAUSALENS: PRODUCT LAUNCHES

TABLE 131 CAUSALENS: DEALS

10.2.9 CAUSALITY LINK

TABLE 132 CAUSALITY LINK: BUSINESS OVERVIEW

TABLE 133 CAUSALITY LINK: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 134 CAUSALITY LINK: PRODUCT LAUNCHES

TABLE 135 CAUSALITY LINK: DEALS

10.2.10 AITIA

TABLE 136 AITIA: BUSINESS OVERVIEW

TABLE 137 AITIA: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 138 AITIA: PRODUCT LAUNCHES

TABLE 139 AITIA: DEALS

10.2.11 XPLAIN DATA

TABLE 140 XPLAIN DATA: BUSINESS OVERVIEW

TABLE 141 XPLAIN DATA: PRODUCTS/SOLUTIONS/SERVICES OFFERED

TABLE 142 XPLAIN DATA: DEALS

*Details on Business overview, Products/Solutions/Services offered, Recent developments & MnM View might not be captured in case of unlisted companies.

10.3 OTHER KEY PLAYERS

10.3.1 PARABOLE.AI

10.3.2 DATMA

10.3.3 INCRMNTAL

10.3.4 CAUSALY

10.3.5 LOGILITY

10.3.6 COGNINO.AI

10.3.7 COGNIZANT

10.3.8 SCALNYX

10.3.9 GEMINOS

11 ADJACENT AND RELATED MARKETS (Page No. - 182)

11.1 AI GOVERNANCE MARKET

11.1.1 MARKET DEFINITION

11.1.2 MARKET OVERVIEW

TABLE 143 AI GOVERNANCE MARKET SIZE AND GROWTH RATE, 2020–2026 (USD MILLION, Y-O-Y%)

11.1.3 AI GOVERNANCE, BY COMPONENT

TABLE 144 AI GOVERNANCE MARKET, BY COMPONENT, 2020–2026 (USD MILLION)

11.1.4 AI GOVERNANCE MARKET, BY SOLUTION

TABLE 145 AI GOVERNANCE MARKET, BY SOLUTION, 2020–2026 (USD MILLION)

11.1.5 AI GOVERNANCE MARKET, BY DEPLOYMENT MODE

TABLE 146 AI GOVERNANCE MARKET, BY DEPLOYMENT MODE, 2020–2026 (USD MILLION)

11.1.6 AI GOVERNANCE MARKET, BY ORGANIZATION SIZE

TABLE 147 AI GOVERNANCE MARKET, BY ORGANIZATION SIZE, 2020–2026 (USD MILLION)

11.1.7 AI GOVERNANCE MARKET, BY VERTICAL

TABLE 148 AI GOVERNANCE MARKET, BY VERTICAL, 2020–2026 (USD MILLION)

11.1.8 AI GOVERNANCE MARKET, BY REGION

TABLE 149 AI GOVERNANCE MARKET, BY REGION, 2020–2026 (USD MILLION)

11.2 ARTIFICIAL INTELLIGENCE MARKET

11.2.1 MARKET DEFINITION

11.2.2 MARKET OVERVIEW

11.2.3 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING

TABLE 150 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2016–2021 (USD BILLION)

TABLE 151 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2022–2027 (USD BILLION)

11.2.4 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY

TABLE 152 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2016–2021 (USD BILLION)

TABLE 153 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2022–2027 (USD BILLION)

11.2.5 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE

TABLE 154 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE, 2016–2021 (USD BILLION)

TABLE 155 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE, 2022–2027 (USD BILLION)

11.2.6 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION SIZE

TABLE 156 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION, 2016–2021 (USD BILLION)

TABLE 157 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION, 2022–2027 (USD BILLION)

11.2.7 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION

TABLE 158 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2016–2021 (USD BILLION)

TABLE 159 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2022–2027 (USD BILLION)

11.2.8 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL

TABLE 160 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2016–2021 (USD BILLION)

TABLE 161 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2022–2027 (USD BILLION)

11.2.9 ARTIFICIAL INTELLIGENCE MARKET, BY REGION

TABLE 162 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2016–2021 (USD BILLION)

TABLE 163 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2022–2027 (USD BILLION)

12 APPENDIX (Page No. - 193)

12.1 DISCUSSION GUIDE

12.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL

12.3 CUSTOMIZATION OPTIONS

12.4 RELATED REPORTS

12.5 AUTHOR DETAILS

The research methodology for the global Causal AI market report involved the use of extensive secondary sources and directories, as well as various reputed open-source databases, to identify and collect information useful for this technical and market-oriented study. In-depth interviews were conducted with various primary respondents, including key opinion leaders, subject matter experts, high-level executives of multiple companies offering Causal AI offerings, and industry consultants to obtain and verify critical qualitative and quantitative information, as well as assess the market prospects and industry trends.

Secondary Research

In the secondary research process, various secondary sources were referred to for identifying and collecting information for the study. The secondary sources included annual reports; press releases and investor presentations of companies; and white papers, certified publications, and articles from recognized associations and government publishing sources.

The secondary research was mainly used to obtain the key information about the industry’s value chain, the market’s monetary chain, the overall pool of key players, market classification and segmentation according to industry trends to the bottom-most level, regional markets, and key developments from both market and technology-oriented perspectives.

Primary Research

In the primary research process, various primary sources from both the supply and demand sides of the Causal AI market ecosystem were interviewed to obtain qualitative and quantitative information for this study. The primary sources from the supply side included industry experts, such as chief executive officers (CEOs), vice presidents (VPs), marketing directors, technology and innovation directors, and related key executives from various vendors providing Causal AI and Causal ML offerings; associated service providers; and system integrators operating in the targeted regions. All possible parameters that affect the market covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

After the complete market engineering (including calculations for market statistics, market breakup, market size estimations, market forecast, and data triangulation), extensive primary research was conducted to gather information and verify and validate the critical numbers arrived at. Primary research was also undertaken to identify and validate the segmentation types; industry trends; key players; the competitive landscape of the market; and key market dynamics, such as drivers, restraints, opportunities, challenges, industry trends, and key strategies.

In the complete market engineering process, both the top-down and bottom-up approaches were extensively used, along with several data triangulation methods, to perform the market estimation and market forecast for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed on the complete market engineering process to record the critical information/insights throughout the report.

The following is the breakup of primary profiles:

Causal AI Market Size, and Share

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

For making market estimates and forecasting the Causal AI market and the other dependent submarkets, top-down and bottom-up approaches were used. The bottom-up procedure was used to arrive at the overall market size of the global Causal AI market, using the revenue from the key companies and their offerings in the market. With data triangulation and validation through primary interviews, the exact value of the overall parent market size was determined and confirmed using this study. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segments.

In the top-down approach, an exhaustive list of all the vendors offering Causal AI and Causal ML was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor’s offerings were evaluated based on the breadth of solution and service offerings, cloud type, and verticals. The aggregate of all the revenues of the companies was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets repository for validation.

In the bottom-up approach, the adoption rate of Causal AI solutions and services among different end-users in key countries with respect to their regions contributing the most to the market share was identified. For cross-validation, the adoption of Causal AI solutions and services among industries, along with different use cases with respect to their regions, was identified and extrapolated. Weightage was given to use cases identified in different regions for the market size calculation.

All the possible parameters that affect the market covered in the research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data. The data is consolidated and added with detailed inputs and analysis from MarketsandMarkets.

The pricing trend is assumed to vary over time.
All the forecasts are made with the standard assumption that the accepted currency is USD.
For the conversion of various currencies to USD, average historical exchange rates are used according to the year specified. For all the historical and current exchange rates required for calculations and currency conversions, the US Internal Revenue Service’s website is used.
All the forecasts are made under the standard assumption that the globally accepted currency, USD, remains constant during the next five years.
Vendor-side analysis: The market size estimates of associated solutions and services are factored in from the vendor side by assuming an average of licensing and subscription-based models of leading and innovative vendors.
Demand/end-user analysis: End users operating in verticals across regions are analyzed in terms of market spending on Causal AI solutions based on some of the key use cases. These factors for the Causal AI tool industry per region are separately analyzed, and the average spending was extrapolated with an approximation based on assumed weightage. This factor is derived by averaging various market influencers, including recent developments, regulations, mergers and acquisitions, enterprise/SME adoption, startup ecosystem, IT spending, technology propensity and maturity, use cases, and the estimated number of organizations per region.

Data Triangulation

After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.

Market Definition

According to RapidMiner, Causal AI is an emerging form of machine learning that strives to go beyond traditional ML models. While traditional techniques identify the extent to which multiple events are related, causal AI identifies the root cause of events by understanding the effects of any variables that may have led to it, providing a much deeper explanation of their true relationship.

Key Stakeholders

Research organizations
Third-party service providers
Technology providers
Cloud services providers
AI consulting companies
Independent software vendors (ISVs)
Service providers and distributors
Application development vendors
System integrators
Consultants/consultancy/advisory firms
Training and education service providers
Support and maintenance service providers
Managed service providers

Report Objectives

To define, describe, and forecast the Causal AI market based on offering, vertical, and region
To provide detailed information about the major factors (drivers, restraints, opportunities, and challenges) influencing the market growth
To analyze subsegments with respect to individual growth trends, prospects, and contributions to the total market
To analyze opportunities in the market for stakeholders and provide the competitive landscape of the market
To forecast the revenue of the market segments with respect to all the five major regions, namely, North America, Europe, Asia Pacific (APAC), the Middle East & Africa (MEA), and Latin America
To profile the key players and comprehensively analyze the recent developments and their positioning related to the Causal AI Market
To analyze competitive developments, such as mergers & acquisitions, product developments, and research & development (R&D) activities, in the market
To analyze the impact of recession across all the regions across the Causal AI Market

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report: